用多感官触觉数据建模人类材料感知，提升机器人触觉能力
Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models
- 构建三模型框架，融合按压、静触和滑动触觉信号
- 热感线索显著提升感知建模与材料分类准确率
- 为机器人触觉系统设计提供可解释的感知指导
人类对材料的触觉感知依赖复杂的多感官触觉线索，但低层触觉信号与感知表征之间的关系仍不明确。这一知识空白限制了触觉在数字环境中的集成以及具备类人触觉能力机器人的发展。本文提出一种可解释的计算框架，利用多感官触觉数据建模人类材料感知与识别。该框架包含三个相互关联的模型：模型1将指尖-表面交互特征映射到心理物理感知属性；模型2基于这些感知表征进行材料分类；模型3直接从触觉特征进行材料分类。结果表明，融合按压、静态接触和滑动交互信息能提高预测准确性，且热感线索对感知建模和材料分类均具有高度信息量。研究强调了热感与柔顺性线索的重要性，这些线索在当前机器人手指和触觉显示中仍被忽视。引入此类线索有助于提升人工系统对人类触觉感知的逼近能力，并指导更符合感知规律的触觉界面设计。
原文摘要 · Abstract (English)
Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual representations remains poorly understood. This knowledge gap hinders the integration of touch in digital environments and the development of robots capable of human-like tactile perception. Here, we present an interpretable computational framework for modeling human material perception and recognition using multisensory touch data. Our framework comprises three interconnected models: Model 1 maps finger-surface interaction features to psychophysical sensory attributes, Model 2 classifies materials based on these perceptual representations, and Model 3 directly classifies materials from tactile features. The results showed that combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and that thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.
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